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The next AI database isn’t another database. it’s a layer over the one you already have. Today we're launching a virtual ai search layer you can deploy over ANY Postgres database. no migrations. no vector DB. no Neo4j. no sync. no bs. One database. Every way to search it.... show more

dale

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16,786 Aufrufe • vor 2 Tagen •via X (Twitter)

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I made a cringy silly Cup Song parody to explain our database infrastructure because pitch vids are BORING This is either marketing innovation or a cry for help (probably no. 2) Polygres is live on Product Hunt 🚀 Turn your database into an infinite context window for AI agents: via Product Hunt 😸 Polygres Dalton Lim Damien

I made a cringy silly Cup Song parody to explain our database infrastructure because pitch vids are BORING This is either marketing innovation or a cry for help (probably no. 2) Polygres is live on Product Hunt 🚀 Turn your database into an infinite context window for AI agents: via Product Hunt 😸 Polygres Dalton Lim Damien

dale

28,911 Aufrufe • vor 21 Tagen

Correa: One of your officers in Maine said to one of the individuals protesting, we're going to put your face in a database. Do you have a database of Americans? Lyons: There is no database.

Correa: One of your officers in Maine said to one of the individuals protesting, we're going to put your face in a database. Do you have a database of Americans? Lyons: There is no database.

Acyn

69,499 Aufrufe • vor 6 Monaten

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

Charly Wargnier

29,415 Aufrufe • vor 1 Monat

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

dale

15,067 Aufrufe • vor 2 Monaten

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

Victor

261,537 Aufrufe • vor 1 Jahr

🚨 Your search history is no longer private. Every Google query, every address, every name, every protest location can sit in a corporate database for years, waiting for the government to weaponize it. @Thomas_Drakel joins us.

🚨 Your search history is no longer private. Every Google query, every address, every name, every protest location can sit in a corporate database for years, waiting for the government to weaponize it. @Thomas_Drakel joins us.

Redacted

13,335 Aufrufe • vor 1 Monat

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

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Avi Chawla

94,290 Aufrufe • vor 9 Monaten

So you can just give it a postgres URL (or sqlite db file) and it will just... explore it and offer to build things. Even with a crazy nonstandard (from Ecto perspective) database. The search bar crashed, I sent the logs to the agent, and boom realtime searching

So you can just give it a postgres URL (or sqlite db file) and it will just... explore it and offer to build things. Even with a crazy nonstandard (from Ecto perspective) database. The search bar crashed, I sent the logs to the agent, and boom realtime searching

Chris McCord

12,758 Aufrufe • vor 1 Jahr

Qullamaggie on Minervini’s 18,000 Chart Database “Wait, Minervini has an 18,000-page chart database on paper? Guys, that’s what you have to do. I think 18,000 may be a bit overkill, and especially on paper. But if you have like one note or ever note or something like that, there’s no reason why you should… you know you… everyone should build a chart database, a pattern database right, at least a couple of thousand charts and setups. And then you trade those setups over and over, again right. Like, I have three setups and variations that I trade over and over again. I don’t do any rocket science at all.”

Qullamaggie on Minervini’s 18,000 Chart Database “Wait, Minervini has an 18,000-page chart database on paper? Guys, that’s what you have to do. I think 18,000 may be a bit overkill, and especially on paper. But if you have like one note or ever note or something like that, there’s no reason why you should… you know you… everyone should build a chart database, a pattern database right, at least a couple of thousand charts and setups. And then you trade those setups over and over, again right. Like, I have three setups and variations that I trade over and over again. I don’t do any rocket science at all.”

Lone

43,557 Aufrufe • vor 10 Monaten

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

140,618 Aufrufe • vor 2 Jahren

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 Aufrufe • vor 1 Jahr

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Avi Chawla

14,463 Aufrufe • vor 13 Tagen

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Harnoor Singh

13,348 Aufrufe • vor 4 Monaten

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 Aufrufe • vor 2 Jahren

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote

29,914 Aufrufe • vor 2 Jahren

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

GitHub

47,160 Aufrufe • vor 5 Monaten

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood

Neon Postgres

26,526 Aufrufe • vor 1 Jahr

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

Charly Wargnier

20,181 Aufrufe • vor 6 Monaten